IMPROVING DETECTION OF INFLUENTIAL NODES IN COMPLEX NETWORKS

Recently an increasing amount of research is devoted to the question of how the most influential nodes (seeds) can be found effectively in a complex network. There are a number of measures proposed for this purpose, for instance, high-degree centrality measure reflects the importance of the network topology and has a reasonable runtime performance to find a set of nodes with highest degree, but they do not have a satisfactory dissemination potentiality in the network due to having many common neighbors (CN) and common neighbors of neighbors (CN). This flaw holds in other measures as well. In this paper, we compare high-degree centrality measure with other well-known measures using ten datasets in order to find a proportion for the common seeds in the seed sets obtained by them. We, thereof, propose an improved high-degree centrality measure (named DegreeDistance) and improve it to enhance accuracy in two phases, FIDD and SIDD, by putting a threshold on the number of common neighbors of already-selected seed nodes and a non-seed node which is under investigation to be selected as a seed as well as considering the influence score of seed nodes directly or through their common neighbors over the non-seed node. To evaluate the accuracy and runtime performance of DegreeDistance, FIDD, and SIDD, they are applied to eight large-scale networks and it finally turns out that SIDD dramatically outperforms other well-known measures and evinces comparatively more accurate performance in identifying the most influential nodes.

[1]  Eric K. Clemons,et al.  Do Online Reviews Reflect a Product's True Perceived Quality? - An Investigation of Online Movie Reviews Across Cultures , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[2]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[3]  O. Sporns.  Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[4]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[5]  Masahiro Kimura,et al.  Extracting Influential Nodes for Information Diffusion on a Social Network , 2007, AAAI.

[6]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[7]  S. Mahadevan,et al.  Identifying influential nodes in weighted networks based on evidence theory , 2013 .

[8]  Florian Probst,et al.  Online social networks: A survey of a global phenomenon , 2012 .

[9]  Ingoo Han,et al.  The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement , 2007, Int. J. Electron. Commer..

[10]  S. Mahadevan,et al.  A modified evidential methodology of identifying influential nodes in weighted networks , 2013 .

[11]  Phillip Bonacich,et al.  Eigenvector-like measures of centrality for asymmetric relations , 2001, Soc. Networks.

[12]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[13]  Florian Probst,et al.  Identifying Key Users in Online Social Networks: A PageRank Based Approach , 2010, ICIS.

[14]  김정년,et al.  Managerial Economics 『관리경제학』 , 1976 .

[15]  Pablo A. Estévez,et al.  Selecting the Most Influential Nodes in Social Networks , 2007, 2007 International Joint Conference on Neural Networks.

[16]  Charu C. Aggarwal,et al.  An Introduction to Social Network Data Analytics , 2011, Social Network Data Analytics.

[17]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[18]  Jennifer Golbeck,et al.  Analyzing the Social Web , 2013 .

[19]  Dimitrios Katsaros,et al.  Detecting Influential Nodes in Complex Networks with Range Probabilistic Control Centrality , 2015 .

[20]  Jun Ma,et al.  Ranking the spreading ability of nodes in complex networks based on local structure , 2014 .

[21]  Huaping Chen,et al.  Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations , 2009, Int. J. Electron. Commer..

[22]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[23]  My T. Thai,et al.  Cheap, easy, and massively effective viral marketing in social networks: truth or fiction? , 2012, HT '12.

[24]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[25]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[26]  Guanrong Chen,et al.  A study of the spreading scheme for viral marketing based on a complex network model , 2010 .

[27]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[28]  Paul J. Laurienti,et al.  Changes in Cognitive State Alter Human Functional Brain Networks , 2011, Front. Hum. Neurosci..

[29]  Oliver Hinz,et al.  New product adoption in social networks: Why direction matters , 2014 .

[30]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[31]  Guangyuan Fu,et al.  A new method to construct co-author networks , 2015 .

[32]  Marián Boguñá,et al.  How to make the top ten: Approximating PageRank from in-degree , 2005, ArXiv.

[33]  Tomasz Arodz,et al.  Identifying influential nodes in a wound healing-related network of biological processes using mean first-passage time , 2015 .

[34]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[35]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[36]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[37]  Yu Zhang,et al.  Identifying Key Users for Targeted Marketing by Mining Online Social Network , 2010, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.

[38]  Florian Probst,et al.  Who will lead and who will follow: Identifying Influential Users in Online Social Networks , 2013, Business & Information Systems Engineering.

[39]  John Scott,et al.  The SAGE Handbook of Social Network Analysis , 2011 .

[40]  Amy Nicole Langville,et al.  Google's PageRank and beyond - the science of search engine rankings , 2006 .

[41]  Duanbing Chen,et al.  Identifying Influential Spreaders by Weighted LeaderRank , 2013, ArXiv.

[42]  Masahiro Kimura,et al.  Extracting influential nodes on a social network for information diffusion , 2009, Data Mining and Knowledge Discovery.